title: Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain.
abstract: OBJECTIVE
  An open-source software system for planning magnetic resonance (MR)-guided laser-induced thermal therapy (MRgLITT) in brain is presented. The system was designed to provide a streamlined and operator-friendly graphical user interface (GUI) for simulating and visualizing potential outcomes of various treatment scenarios to aid in decisions on treatment approach or feasibility.
  
  
  METHODS
  A portable software module was developed on the 3D Slicer platform, an open-source medical imaging and visualization framework. The module introduces an interactive GUI for investigating different laser positions and power settings as well as the influence of patient-specific tissue properties for quickly creating and evaluating custom treatment options. It also provides a common treatment planning interface for use by both open-source and commercial finite element solvers. In this study, an open-source finite element solver for Pennes' bioheat equation is interfaced to the module to provide rapid 3D estimates of the steady-state temperature distribution and potential tissue damage in the presence of patient-specific tissue boundary conditions identified on segmented MR images.
  
  
  RESULTS
  The total time to initialize and simulate an MRgLITT procedure using the GUI was [Formula: see text]5 min. Each independent simulation took [Formula: see text]30 s, including the time to visualize the results fused with the planning MRI. For demonstration purposes, a simulated steady-state isotherm contour [Formula: see text] was correlated with MR temperature imaging (N = 5). The mean Hausdorff distance between simulated and actual contours was 2.0 mm [Formula: see text], whereas the mean Dice similarity coefficient was 0.93 [Formula: see text].
  
  
  CONCLUSIONS
  We have designed, implemented, and conducted initial feasibility evaluations of a software tool for intuitive and rapid planning of MRgLITT in brain. The retrospective in vivo dataset presented herein illustrates the feasibility and potential of incorporating fast, image-based bioheat predictions into an interactive virtual planning environment for such procedures.
authors.affiliation: Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA, eyeniaras@gmail.com.
authors.forename: E
authors.initials: E
authors.lastname: Yeniaras
authors.forename: D T
authors.initials: DT
authors.lastname: Fuentes
authors.forename: S J
authors.initials: SJ
authors.lastname: Fahrenholtz
authors.forename: J S
authors.initials: JS
authors.lastname: Weinberg
authors.forename: F
authors.initials: F
authors.lastname: Maier
authors.forename: J D
authors.initials: JD
authors.lastname: Hazle
authors.forename: R J
authors.initials: RJ
authors.lastname: Stafford
mesh_terms: D001921:Brain / Q000601:surgery*; D006801:Humans; D053685:Laser Therapy / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012189:Retrospective Studies; D012984:Software